Project Summary/Abstract Surgical operations can put patients at high risk of infections and other complications. However, studies have shown that half or more of surgical infections are discovered after hospital discharge. The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) began in 2005 with the goal of assisting hospitals with identifying and preventing surgical complications. Each participating ACS NSQIP hospital assigns a surgical clinical nurse reviewer to collect preoperative through 30-day postoperative data on a sample of surgical patients in order to risk adjust postoperative complications so that they can be compared across participating hospitals. Although at large volume hospitals these samples might represent only 10-15% of all surgical cases, these data are considered to be the current gold standard for accurate identification and comparison of postoperative complications. Unfortunately, chart review is time-consuming and costly and, as a result, cannot be scaled up easily to cover all surgical patients. The goal of this project is to learn from the ACS NSQIP data in order to develop electronic algorithms for identifying postoperative infections that can be scaled up easily and inexpensively. Development of such algorithms will also permit evaluation of interventions that intend to impact large populations of patients at risk of postoperative infections. Considering the large number of available binary classification algorithms for data mining that are easy to implement, it is paramount to consider new methods for identifying surgical infections electronically. Furthermore, postoperative infections are rare, and occur in about 7% of operated patients; therefore, it is difficult to identify models that classify infections well. Sampling techniques are commonly used in conjunction with classification models in order to improve sensitivity and positive predictive value. We believe that modern statistical techniques for classification combined with strategic sampling, and the use of ICD9 and ICD10 codes and pharmacy data, will improve upon existing methods for electronically identifying postoperative infections. The aims of this proposal are (1a) to develop algorithms for identifying surgical infections using machine learning techniques, (1b) to develop models for specific types of postoperative infections collected in the ACS NSQIP data separately, which include SSI, urinary tract infection, pneumonia and sepsis and (2) to validate these models in prospective ACS NSQIP data.